Multidimensional Image Indexing Using SR-Tree Algorithm for Content-Based Image Retrieval System

Vanitha, J.; Senthilmurugan, M.

doi:10.1007/978-981-10-4741-1_8

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…Let 𝑔 0 = the centre pixel 𝐼 while 𝑔 ℎ and 𝑔 𝑣 = the horizontal and vertical pixels, respectively. The first order derivatives magnitude at the centre pixel can then be represented as: while the direction at the centre pixel is denoted as: (7) (8) from ( 7) and ( 8), four different directions of the centre pixel can be calculated.…”

Section: Low-level Features a Hogmentioning

confidence: 99%

“…The CBIR is also referred to as query by image content (QBIC) or content-based visual information retrieval (CBVIR) [5]. It is a research field that has received much attention over the last 20 years [6,7]. Context diversity in huge databases seems to be the trade-off between achieving relevant and intended results and achieving different results that cover different search areas [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Facial image retrieval on semantic features using adaptive mean genetic algorithm

Shnan¹,

Rassem²,

Zulkifli³

2019

TELKOMNIKA

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The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

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Section: Low-level Features a Hogmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Facial image retrieval on semantic features using adaptive mean genetic algorithm

Shnan¹,

Rassem²,

Zulkifli³

2019

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…Let = the center pixel while and = the horizontal and vertical pixels, respectively. The first order derivatives magnitude at the center pixel can then be represented as: (6) while the direction at the center pixel is denoted as: (7) (8) From equations 7 and 8, four different directions of the center pixel can be calculated.…”

Section: Ltrpmentioning

confidence: 99%

“…The retrieval of an image can either be text or content based. The text-based image retrieval (TBIR) involves the retrieval of an image based on its textual information, while the content-based image retrieval (CBIR) uses some of the contents of the image such as its shape, color, texture, or spatial information to retrieve the image [6] [7]. The CBIR is also referred to as query by image content (QBIC) or content-based visual information retrieval (CBVIR) [3].…”

Section: Introductionmentioning

confidence: 99%

Facial Image Retrieval on Semantic Features Using Adaptive Genetic Algorithm

Shnan¹,

Rassem²

2018

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The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can be either content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Viola-Jones algorithm. In this study, the average PSNR value obtained after applying Wiener filter was 45.29. The performance of the AGA was evaluated based on its precision, Fmeasure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

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Multidimensional Image Indexing Using SR-Tree Algorithm for Content-Based Image Retrieval System

Cited by 2 publications

References 12 publications

Facial image retrieval on semantic features using adaptive mean genetic algorithm

Facial image retrieval on semantic features using adaptive mean genetic algorithm

Facial Image Retrieval on Semantic Features Using Adaptive Genetic Algorithm

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